Handwritten Gurmukhi Characters Research Articles

A Novel Feature Extraction Technique for Offline Handwritten Gurmukhi Character Recognition

A novel feature extraction technique is presented in this paper for an offline handwritten Gurmukhi character recognition system. Handwritten character recognition is a complex task because of various writing styles of different individuals. To select a set of features is an important step for implementing a handwriting recognition system. In this work, we have extracted various topological features, namely, peak-extent features, shadow features and centroid features. A new feature set is also proposed by using horizontal peak extent features and the vertical peak extent features. For classification, we have used k-NN and Linear-SVM classifiers. In view of learning and simplification capabilities of multi layer perceptrons (MLPs), MLPs based pattern classifier is also used for classification. In the present work, we have taken 7,000 samples of offline handwritten Gurmukhi characters for training and testing. Proposed system achieves a maximum recognition accuracy of 95.62% using SVM with linear kernel classifier. By using k-NN and MLPs, a maximum recognition accuracy of 95.48% and 94.74%, respectively, has been achieved with five-fold cross validation.

PCA-based Offline Handwritten Character Recognition System

Principal component analysis (PCA) has been used widely in pattern recognition to reduce the extent of the data. In this paper, we explore using this technique to recognize offline handwritten Gurmukhi characters, and a system for offline handwritten Gurmukhi character recognition using PCA is proposed. The system first prepares a skeleton of the character so that meaningful feature information about the character can be extracted. For classification, we used k-nearest neighbor, Linear-SVM, polynomial-SVM and RBF-SVM based approaches and combinations of these approaches. In this work, we collected 16,800 samples of isolated offline handwritten Gurmukhi characters. These samples were divided into three categories. In category 1 (5600 samples), each Gurmukhi character was written 100 times by a single writer. In category 2 (5600 samples), each Gurmukhi character was written 10 times by 10 different writers, and in category 3 (5600 samples), each Gurmukhi character was written by 100 different writers. The set of the basic 35 akhars of Gurmukhi has been considered here. A partitioning strategy for selecting the training and testing patterns is also explored in this work. We used zoning, diagonal, directional, transition, intersection and open end point, parabola curve fitting?based and power curve fitting?based feature extraction in order to find the feature set for a given character. The proposed system achieves a recognition accuracy of 99.06% in category 1, 98.73% in category 2 and 78.30% in category 3.

AN EFFICIENT POST PROCESSING ALGORITHM FOR ONLINE HANDWRITING GURMUKHI CHARACTER RECOGNITION USING SET THEORY

In this paper, a post processor for accuracy of character recognition of real-time online Gurmukhi script has been developed. Our analysis is based on dataset consisting of 184 samples of each 45 characters of Gurmukhi script collected from four different categories of writers. Based on this extensive study, we propose an efficient algorithm for online handwritten Gurmukhi character recognition that achieves promising recognition accuracy of 95.6% for single character stroke sequencing. Beside character recognition the contribution in this paper is summarized in two folds as (i) the proposed scheme resolves stroke sequencing, (ii) overwritten strokes are identified and resolved. Moreover, for every stroke, complexity of adding new stroke for Gurmukhi character formation has been computed to be O(n).

HMM-based online handwritten gurmukhi character recognition

This paper presents a hidden Markov model-based online handwritten character recognition for Gurmukhi script. We discuss a procedure to develop a hidden Markov model database in order to recognize Gurmukhi characters. A test with 60 handwritten samples, where each sample includes 41 Gurmukhi characters, shows a 91.95% recognition rate, and an average recognition speed of 0.112 seconds per stroke. The hidden Markov model database has been developed in XML using 5330 Gurmukhi characters. This work shall be useful to implement a hidden Markov model in online handwriting recognition and its software development.

Recognizing Online Handwritten Gurmukhi Characters Using Comparison of Small Line Segments

This paper presents a method to recognize online handwritten Gurmukhi characters. Gurmukhi is the script of Punjabi language which is widely spoken across the globe. The proposed method is called small line segments and based on idea of chain code and elastic matching techniques. Using this method, we have obtained an overall recognition rate of 94.59% for a set of 2460 Gurmukhi characters collected from 60 writers. In recognizing a single stroke, the average speed using this method is approximately 0.156 seconds.